Geometry-Based Vehicle-to-Vehicle Channel Modeling for Large-Scale Simulation

Abstract

Large-scale Vehicular Ad Hoc Network (VANET) simulators by and large employ simple statistical channel models. By design, such models do not account for specific objects in the region of interest when estimating the channel. While computationally efficient, these models were shown to be unable to provide satisfactory accuracy on a link level for typical VANET scenarios. Specifically, experimental studies have shown that both large static objects (e.g., buildings and foliage) as well as mobile objects (surrounding vehicles) have a profound impact on the quality of vehicle-to-vehicle (V2V) channels. While several recently proposed large-scale V2V channel models account for static objects (e.g., buildings) in the simulated area, there is a need for a comprehensive model that takes into account both the static and the mobile objects. To fill this gap, we designed a geometry-based, computationally manageable V2V channel model that uses the real-world locations and the actual dimensions of vehicles, buildings, and foliage to simulate the V2V channel more realistically. We use the outlines of the modeled objects to form spatial tree structures for efficient manipulation of geographic data. We distinguish and model separately the following three link types: line of sight (LOS), non-LOS due to vehicles, and non-LOS due to static objects. Apart from the model for large-scale signal variations, we also propose a simple model for small-scale signal variation using the number and size of the objects around the communicating vehicles. We validate the models against extensive measurements performed in urban, suburban, highway, and open space environment. We provide the complete simulation recipe for the implementation of the model in simulators. Finally, we implement the model in Matlab and show that it scales well by simulating networks with tens of thousands of objects and hundred thousand communicating vehicle pairs using commodity hardware.

Modeling the Evolution of Line-of-Sight Blockage for V2V Channels

“…By using real roadways and traffic rules and employing vehicular traffic dynamics models such as car-following [18], SUMO is capable of generating accurate vehicle positions, speeds, inter-vehicle distance, acceleration, overtaking and lane-changing maneuvers, etc. Previous work (e.g., [5], [12]) has shown that V2V channel characteristics are affected by the density of vehicular traffic. To that end, for each environment, we generated three traffic densities, qualitatively characterized as low, medium and high.…”

“…We start our analysis by acknowledging that V2V links can have their LOS blocked by two distinct object types, static and mobile, which have distinct impact on V2V links [5]. Furthermore, static objects such as buildings, trees, etc., typically block the LOS for V2V links between vehicles that are on different roads (e.g., perpendicular roads joined by intersections).…”

“…We use the LOS blockage classification provided by GEMV 2 , a freely available, geometry-based V2X propagation modeling tool [5]. GEMV 2 uses the outlines of vehicles, buildings, and foliage to distinguish between LOS, NLOSv, and NLOSb links.…”

“…On the other hand, time evolution and spatial consistency are inherently supported by geometry-based deterministic models (e.g., [4], [5]). However, for performing efficient simulations it is also beneficial to have a model that can generate consistent channel realizations for a chosen environment without the need for complex geometric modeling using location-specific map information.…”

“…We employ a Markov chain comprised of three states: i) LOS; ii) NLOSb -non-LOS due to static objects (e.g., buildings, trees, etc. ); and iii) NLOSvnon-LOS due to mobile objects (vehicles), since these three states were shown to have distinct path loss and shadowing parameters [5], [7]. To calculate LOS and transition probability statistics 1 , we perform geometry-based deterministic simulations of LOS blockage and extract the parameters from real cities and highways for LOS blockage and transition probabilities.…”